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CycleGEAN: Cycle Generative Enhanced Adversarial Network for Voice Conversion
Cycle Generative Adversarial Network (CycleGAN) for voice conversion (VC) task only used discriminators to identify whether the input …
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CycleGEAN: Cycle Generative Enhanced Adversarial Network for Voice Conversion
Reconstructing Dual Learning for Neural Voice Conversion Using Relatively Few Samples
This paper introduces a dual learning system for neural voice conversion (DualVC) using relatively few samples based on the symmetry of …
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Reconstructing Dual Learning for Neural Voice Conversion Using Relatively Few Samples
TGAVC: Improving Autoencoder Voice Conversion with Text-Guided and Adversarial Training
Non-parallel many-to-many voice conversion remains an interesting but challenging speech processing task. Recently, AutoVC, a …
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TGAVC: Improving Autoencoder Voice Conversion with Text-Guided and Adversarial Training
Dropout Regularization for Self-Supervised Learning of Transformer Encoder Speech Representation
Predicting the altered acoustic frames is an effective way of self-supervised learning for speech representation. However, it is …
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Dropout Regularization for Self-Supervised Learning of Transformer Encoder Speech Representation
Speech2Video: Cross-Modal Distillation for Speech to Video Generation
This paper investigates a novel task of talking face video generation solely from speeches. The speech-to-video generation technique …
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Speech2Video: Cross-Modal Distillation for Speech to Video Generation
Variational Information Bottleneck for Effective Low-Resource Audio Classification
Large-scale deep neural networks (DNNs) such as convolutional neural networks (CNNs) have achieved impressive performance in audio …
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Variational Information Bottleneck for Effective Low-Resource Audio Classification
A Language Model Based Pseudo-Sample Deliberation for Semi-supervised Speech Recognition
End-to-end modeling requires tremendous amounts of transcribed speech to achieve an automatic speech recognition (ASR) model with high …
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A Language Model Based Pseudo-Sample Deliberation for Semi-supervised Speech Recognition
CACnet: Cube Attentional CNN for Automatic Speech Recognition
End-to-end models have been widely used in Automatic Speech Recognition (ASR). Convolutional Neural Networks (CNNs) can effectively use …
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CACnet: Cube Attentional CNN for Automatic Speech Recognition
Loss Prediction: End-to-End Active Learning Approach For Speech Recognition
End-to-end speech recognition systems usually require huge amounts of labeling resource, while annotating the speech data is …
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Loss Prediction: End-to-End Active Learning Approach For Speech Recognition
Transfer Ability of Monolingual Wav2vec2.0 for Low-resource Speech Recognition
Recently, there are several domains that have their own feature extractors, such as ResNet, BERT, and GPT-x, which are widely used for …
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Transfer Ability of Monolingual Wav2vec2.0 for Low-resource Speech Recognition